While cancer-triggering mutations often inactivate tumor suppressor genes, they also frequently delete genes essential for survival. This causes cancer cells to rely on other genes that function like the essential genes lost as collateral damage in the cancer-causing mutation. Complete dependence on these paralogs constitutes a unique vulnerability in cancer cells. These paralogs are therefore attractive targets for treatments that could specifically kill cancer cells, leaving normal cells unharmed. Yet, there are currently no methods available to detect such collateral lethal genes.
A collaborative team led by Deepak Nagrath, PhD, an associate professor of biomedical engineering who works on ovarian cancer at the North Campus Research Center (NCRC) in the University of Michigan, and Xiongbin Lu, PhD, a professor of breast cancer innovation at the Indiana University school of medicine, has developed a method called CLIM (collateral lethal gene identification via metabolic fluxes) that identifies collateral lethal genes. They have used CLIM to successfully reveal a collateral lethal gene (MTHFD2) in ovarian cancers where the essential gene UQCR11 is lost as part of a larger deletion commonly found in patients.
The findings were published in the journal Nature Metabolism on September 21, 2022 “Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer.”
The researchers identified consistent loss of a chunk of chromosome 19’s short arm (19p13.3) in clusters of patients with ovarian cancer. UQCR11, a gene required for the generation of cellular energy through a process called oxidative phosphorylation, lies within this deleted fragment. This led the researchers to hypothesize that ovarian cancer cells with the 19p13.3 deletion must rely on other metabolic genes to fulfill their energy needs. This in turn led them develop a machine-learning algorithm to find a collateral lethal metabolic target specific to UQCR11-null ovarian cancer.
“We show that MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+, and demonstrate the regulation of systemic metabolic activity by the paralogue metabolic pathway maintaining metabolic flux compensation,” the authors noted.
The researchers confirm the collateral lethality of the UQCR11–MTHFD2 genetic pair collateral in vivo in mice. They showed, inhibition of MTHFD2 led to a complete remission of UQCR11-deleted ovarian tumors in mice. Inhibiting MTHFD2 essentially shuts down the cancer cell’s energy source.
Lu’s team at Indiana University School of Medicine helped validate Nagrath’s findings. Lu’s team developed genetically modified cell and animal models of ovarian cancers with the deletion. Six out of six mice they tested showed complete cancer remission.
“Using CLIM’s machine learning and genome-scale metabolic flux analysis, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumors,” the authors concluded.
The CLIM method provides a new approach to identify back-up metabolic pathways critical for the survival of cancer cells that can be therapeutically shut down to combat cancer, in a mutation-specific manner. The precision method exploits the very deletions that render cancer cells immortal.
“When a direct replacement for the deleted metabolic gene is not available, our algorithms use a mathematical model of the cancer cells’ metabolism to predict the paralogous metabolic pathway they might use,” said Abhinav Achreja, PhD, a biomedical engineering research fellow at the University of Michigan and lead author on the research paper. “These metabolic pathways are important to the cancer cells and can be targeted selectively.”
Although the current study focused on ovarian cancer with a specific mutation, the CLIM method can be used to expose analogous vulnerabilities across most forms of cancer that are essential for the survival of cancer cells, resulting in precision treatment plans for a host of malignancies.
This research was funded by the National Cancer Institute, University of Michigan Precision Health Scholars Award, and Forbes Institute of Cancer Discovery.
